In intelligent visual surveillance, people are usually observed with distant from multiple cameras. Generally, face recognition techniques are used to identify the individuals. However, the shortcomings of this approach are low-resolution face image, due to large distances, unexpected view angle, and occlusion. Therefore, Gait is considered to be the most suitable biometrics alternative in visual surveillance.Today, deep learning plays a key role in many computer vision problems such as image super resolution, image classification, and object recognition. Instead of hand crafted features such as SIFT, HOG, SURF, etc., deep learning algorithms extract a useful representation of the content of images.In this project, the goal is to apply deep learning for gait recognition. People are imaged by Kinect that delivers us a color image and a depth map (i.e., RGB-D image). A sample n image of an RGB-D image of several people is presented below. The idea is to apply a novel or existing deep learning algorithm to learn a new image representation from both RGB and Depth images in order to increase the accuracy of gait recognition. It is recommended to use one of the existing libraries for deep learning like caffe or torch for implementing the algorithm.

Preliminary knowledge in Machine learning and good programming skill in Matlab and C++ is highly required. For further questions, please write me an email to set an appointment.

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